Presence-absence data

library(plotly)
library(packcircles)
library(ggiraph)
library(countrycode)
library(tmap)
tmap_mode('view')
library(sf)
sf::sf_use_s2(FALSE)
library(tidyverse)

Data

List of species

carnivores <- read_csv('data/carnivores.csv')

Number of carnivore species in the Neotropics = 71

Countries

countries <- st_read('data/latam.gpkg', layer = 'countries', quiet = T)

Number of countries in the Neotropics = 21

Presence only records

PA <- read_csv('data/data_PA.csv')

data_PA <- PA %>% 
  st_as_sf(coords=c('decimalLongitude', 'decimalLatitude')) %>%
  st_set_crs(4326)

Number of PA records of presences (= 1) = 25998
Number of PA records of absences (= 0) = 19470
Number of species represented in the PA data = 50 (70.4 %)

Sources

Top 10 data sources (dataset names)

PA %>% 
  count(datasetName, dataSource) %>% 
  arrange(desc(n)) %>% filter(!is.na(datasetName)) %>% 
  slice_head(n=10) %>% 
  rename(`Dataset name`= datasetName, 
         `Data source` = dataSource) %>% 
  mutate(`%` = formattable::percent(n / sum(n))) %>% 
  kableExtra::kbl()

Dataset name	Data source	n	%
Are tree plantations promoting homogenization of mammal assemblages between regions with contrasting environments?	literature	6120	26.96%
Oncafari	data paper	3333	14.68%
Hufnagel,Ludmila_LEC-QF	data paper	2915	12.84%
IgorPfeiferCoelho	data paper	2880	12.68%
WCS-Bolivia	data paper	2005	8.83%
Equipe_SC	data paper	1447	6.37%
Tick-tock… says the moon and the sun: Daily activity patterns of mid-large-sized mammals in grassland-dominated landscapes afforested with Eucalyptus	literature	1040	4.58%
Santiago Espinosa	data paper	1034	4.55%
Cronemberger_Parnaso	data paper	1032	4.55%
Selecao Natural	data paper	898	3.96%

Number of datasets involved in the PA data = 271
Number of datasets involved in the PA data from the literature = 64
Number of datasets involved in the PA data from the data paper = 207

Geographic coverage

Data are summarised per camera trap study (datasetName)

tmap_mode('view')
data_PA %>% 
  group_by(datasetName) %>% 
  summarise(country = first(country), 
            eventDurationValue = first(eventDurationValue),
            eventDurationUnit = first(eventDurationUnit),
            samplingEffortValue = first(samplingEffortValue),
            samplingEffortUnit = first(samplingEffortUnit),
            totalAreaSampledValue = first(totalAreaSampledValue),
            totalAreaSampledUnit = first(totalAreaSampledUnit),
            dataSource = first(dataSource),
            n_species=n_distinct(scientificName)) %>% st_cast('POINT') %>% 
  tm_shape() +
    tm_dots(size = 0.25) + 
    tm_shape(countries) +
    tm_borders(col='grey60', alpha = 0.2) +
    tm_layout(asp = 0, legend.outside= T, legend.outside.size = 0.1)

PA %>% 
    group_by(country) %>%
    summarise(`Number of records`= n(),
              `presences`= sum(presence==1), 
              `absences`= sum(presence==0), 
              `Number of species`= n_distinct(scientificName)) %>% 
  mutate(code=countrycode(country,
                          origin = 'country.name',
                          destination = 'iso2c')) %>%
  relocate(country, code) %>% 
  kableExtra::kbl()

country	code	Number of records	presences	absences	Number of species
Argentina	AR	7373	1365	6008	22
Belize	BZ	5	5	0	5
Bolivia	BO	1858	1858	0	22
Brazil	BR	27563	17341	10222	27
Chile	CL	23	23	0	7
Colombia	CO	549	405	144	17
Costa Rica	CR	739	306	433	22
Ecuador	EC	1469	751	718	20
Guatemala	GT	642	642	0	12
Guyana	GY	28	22	6	8
Honduras	HN	11	5	6	11
Mexico	MX	1075	1049	26	23
Panama	PA	36	26	10	15
Paraguay	PY	117	114	3	15
Peru	PE	1401	937	464	25
Suriname	SR	970	200	770	15
Uruguay	UY	1494	834	660	12
Venezuela	VE	115	115	0	8

Number of countries represented in the PA data = 18

Taxonomic coverage

Considering only the presences.

data <- PA %>% filter(presence==1) %>% 
  group_by(family) %>% 
  summarise(n_records=n(), n_species=n_distinct(scientificName)) 

packing <- circleProgressiveLayout(data$n_records, sizetype='area')
data <- cbind(data, packing)
packing$radius <- 0.95*packing$radius

dat.gg <- circleLayoutVertices(packing, npoints=50)
data$text <- paste('family: ', data$family, 
                   '\n', 'records:', data$n_records, 
                   '\n', 'species:', data$n_species)

plot.gg <- ggplot() + 
  geom_polygon_interactive(data = dat.gg,
                           aes(x, y, group = id, fill=id,
                               tooltip = data$text[id],
                               data_id = id), colour = 'black', alpha = 0.6) +
  scale_fill_continuous_interactive(type = 'viridis') +
  geom_text(data = data, aes(x, y, label = gsub('Group_', '', family)), size=3, color='black') +
  theme_void() + 
  theme(legend.position='none', plot.margin=unit(c(0,0,0,0),'cm') ) + 
  coord_equal()

x <- girafe(ggobj = plot.gg)
x

PA %>%
  group_by(scientificName, family) %>% count(presence) %>% 
  mutate(presence = if_else(presence == 1, 'presence', 'absence')) %>% 
  pivot_wider(names_from = presence, 
              values_from = n, values_fill = 0) %>% 
  rename(`Number of absences`= absence,
         `Number of presences`= presence,) %>% 
  kableExtra::kbl()

scientificName	family	Number of absences	Number of presences
Atelocynus microtis	Canidae	207	290
Bassaricyon alleni	Procyonidae	0	1
Bassariscus astutus	Procyonidae	0	40
Bassariscus sumichrasti	Procyonidae	15	1
Canis latrans	Canidae	50	89
Cerdocyon thous	Canidae	1261	4065
Chrysocyon brachyurus	Canidae	1046	480
Conepatus chinga	Mephitidae	631	189
Conepatus leuconotus	Mephitidae	0	71
Conepatus semistriatus	Mephitidae	361	380
Eira barbara	Mustelidae	1477	2223
Galictis cuja	Mustelidae	267	101
Galictis vittata	Mustelidae	126	44
Herpailurus yagouaroundi	Felidae	1622	787
Leopardus colocola	Felidae	3	1
Leopardus geoffroyi	Felidae	601	249
Leopardus guigna	Felidae	0	6
Leopardus guttulus	Felidae	702	742
Leopardus jacobita	Felidae	0	11
Leopardus pardalis	Felidae	1298	4242
Leopardus tigrinus	Felidae	506	224
Leopardus wiedii	Felidae	1306	918
Lontra longicaudis	Mustelidae	467	86
Lontra provocax	Mustelidae	0	1
Lycalopex culpaeus	Canidae	0	41
Lycalopex grisea	Canidae	1	11
Lycalopex gymnocerca	Canidae	411	508
Lycalopex sechurae	Canidae	0	34
Lycalopex vetula	Canidae	209	127
Lyncodon patagonicus	Mustelidae	0	3
Lynx rufus	Felidae	2	56
Mephitis macroura	Mephitidae	2	28
Nasua narica	Procyonidae	22	303
Nasua nasua	Procyonidae	1298	2908
Nasua olivacea	Procyonidae	16	3
Neogale africana	Mustelidae	1	1
Neogale frenata	Mustelidae	17	26
Panthera onca	Felidae	1410	2074
Potos flavus	Procyonidae	31	17
Procyon cancrivorus	Procyonidae	1611	1661
Procyon lotor	Procyonidae	38	120
Pteronura brasiliensis	Mustelidae	273	22
Puma concolor	Felidae	1602	2478
Speothos venaticus	Canidae	566	64
Spilogale angustifrons	Mephitidae	0	6
Spilogale gracilis	Mephitidae	0	11
Spilogale pygmaea	Mephitidae	2	3
Tremarctos ornatus	Ursidae	8	26
Urocyon cinereoargenteus	Canidae	4	204
Ursus americanus	Ursidae	0	22

Species not covered

# species not included  
carnivores %>% 
  filter(!species %in% unique(PA$scientificName)) %>%
  select(species, family) %>% arrange(family, species) %>% 
  kableExtra::kbl()

species	family
Canis lupus	Canidae
Lycalopex fulvipes	Canidae
Vulpes macrotis	Canidae
Leopardus braccatus	Felidae
Leopardus emiliae	Felidae
Leopardus fasciatus	Felidae
Leopardus garleppi	Felidae
Leopardus narinensis	Felidae
Leopardus pajeros	Felidae
Mephitis mephitis	Mephitidae
Spilogale interrupta	Mephitidae
Spilogale leucoparia	Mephitidae
Spilogale yucatanensis	Mephitidae
Enhydra lutris	Mustelidae
Lontra canadensis	Mustelidae
Neogale felipei	Mustelidae
Taxidea taxus	Mustelidae
Bassaricyon gabbii	Procyonidae
Bassaricyon medius	Procyonidae
Bassaricyon neblina	Procyonidae
Procyon pygmaeus	Procyonidae

Temporal coverage

coef <- 150

plot_records <- ggplot(PA, aes(x = dateStart, y = scientificName, colour = scientificName)) +
  geom_segment(aes(xend = dateEnd, yend = scientificName), colour = 'black') +
  geom_point(size = 3) +
  geom_point(aes(x = dateEnd), size = 3) +
  theme_bw() +
  theme(legend.position = 'none') +
  labs(x='', y='')

plot_records <- ggplotly(plot_records)
plot_records